---
title: "HCAMP Cluster Comparison"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: scroll
theme: united
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(here)
library(janitor)
library(rio)
library(colorblindr)
library(gghighlight)
library(forcats)
library(ggrepel)
library(knitr)
library(kableExtra)
library(reactable)
library(plotly)
library(glue)
library(fs)
library(rstatix)
library(ggpubr)
library(writexl)
library(remotes)
library(profvis)
theme_set(theme_minimal(15) +
theme(legend.position = "bottom",
panel.grid.major.x = element_line(color = "gray60"),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank())
)
```
```{r global, include=FALSE}
#all clean sims data
sims_concussion_data <- read_csv(here("data", "sims_concussion_data.csv"))
sims_concussion_data <- sims_concussion_data %>%
mutate(age = as.factor(age))
#impact data only
one_impact_test <- read_csv(here("data", "one_post_injury_impact_test.csv"))
two_impact_test <- read_csv(here("data", "two_post_injury_impact_test.csv"))
three_impact_test <- read_csv(here("data", "three_post_injury_impact_test.csv"))
four_impact_test <- read_csv(here("data", "four_post_injury_impact_test.csv"))
# impact sims merge data
one_impact_sims <- read_csv(here("data", "one_post_injury_test_impact_sims_merge.csv"))
two_impact_sims <- read_csv(here("data", "two_post_injury_test_impact_sims_merge.csv"))
three_impact_sims <- read_csv(here("data", "three_post_injury_test_impact_sims_merge.csv"))
four_impact_sims <- read_csv(here("data", "four_post_injury_test_impact_sims_merge.csv"))
# pcss items
pcss_items <- import(here("data", "pcss_items.xlsx"),
setclass = "tbl_df")
pcss_items_data <- import(here("data", "pcss_items_data.xlsx"),
setclass = "tbl_df")
pcss_cluster <- import(here("data", "cluster_scores.xlsx"),
setclass = "tbl_df")
```
# Symptom Comparisons
Sidebar {.sidebar}
------------
Symptoms that appear to be rated with higher severity more consistently:
1. Headache
2. Difficulty concentrating
3. Difficulty remembering
4. Irritability
5. Light sensitivity
Symptoms from the headache-migraine, cognitive and anxiety-mood clusters appear to remain elevated throughout recovery.
The vestibular cluster symptom of dizziness appears to be rated highest at the time of first testing and lessen in severity across the recovery time.
```{r, include=FALSE}
one_impact_test_cluster <- one_impact_test %>%
pivot_longer(
cols = c(32:37),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
) %>%
select(c(1:8, 37, 38))
one_impact_test_symptoms <- one_impact_test %>%
pivot_longer(
cols = c(9:30),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(c(1:15, 21, 22))
tst_aov <- aov(total_symptom_score_post_injury_1 ~ post_injury_1_rating,
data = one_impact_test_symptoms)
summary(tst_aov)
one_impact_test_symptoms <- left_join(one_impact_test_symptoms, pcss_items_data)
```
```{r, include=FALSE}
two_impact_test_1 <- two_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
two_impact_test_2 <- two_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
two_impact_test_1_symptoms <- two_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
two_impact_test_1_symptoms <- left_join(two_impact_test_1_symptoms, pcss_items_data)
two_impact_test_2_symptoms <- two_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
two_impact_test_2_symptoms <- left_join(two_impact_test_2_symptoms, pcss_items_data)
```
```{r, include=FALSE}
three_impact_test_1 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
three_impact_test_2 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
three_impact_test_3 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_3"))
three_impact_test_1_symptoms <- three_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
three_impact_test_1_symptoms <- left_join(three_impact_test_1_symptoms, pcss_items_data)
three_impact_test_2_symptoms <- three_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
three_impact_test_2_symptoms <- left_join(three_impact_test_2_symptoms, pcss_items_data)
three_impact_test_3_symptoms <- three_impact_test_3 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_3_rating",
names_pattern = "(.*)_post_injury_3"
) %>%
select(-c(14:18))
three_impact_test_3_symptoms <- left_join(three_impact_test_3_symptoms, pcss_items_data)
```
```{r, include=FALSE}
four_impact_test_1 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
four_impact_test_2 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
four_impact_test_3 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_3"))
four_impact_test_4 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_4"))
four_impact_test_1_symptoms <- four_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
four_impact_test_1_symptoms <- left_join(four_impact_test_1_symptoms, pcss_items_data)
four_impact_test_2_symptoms <- four_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
four_impact_test_2_symptoms <- left_join(four_impact_test_2_symptoms, pcss_items_data)
four_impact_test_3_symptoms <- four_impact_test_3 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_3_rating",
names_pattern = "(.*)_post_injury_3"
) %>%
select(-c(14:18))
four_impact_test_3_symptoms <- left_join(four_impact_test_3_symptoms, pcss_items_data)
four_impact_test_4_symptoms <- four_impact_test_4 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_4_rating",
names_pattern = "(.*)_post_injury_4"
) %>%
select(-c(14:18))
four_impact_test_4_symptoms <- left_join(four_impact_test_4_symptoms, pcss_items_data)
```
Row {.tabset}
-----------------------------------------------------------------------
### One Test
```{r, include=FALSE}
all_symptom_tbl <- one_impact_test_symptoms %>%
group_by(gender, cluster, symptom) %>%
summarize(Mean = mean(post_injury_1_rating),
SD = sd(post_injury_1_rating),
Min = min(post_injury_1_rating),
Max = max(post_injury_1_rating),
Total = length(post_injury_1_rating)) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
symptom_tbl <- function(df, a, b, c, d) {
df %>%
group_by({{a}}, {{b}}, {{c}}) %>%
summarize(Mean = mean({{d}}),
SD = sd({{d}}),
Min = min({{d}}),
Max = max({{d}}),
Total = length({{d}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
symptom_tbl_over_three <- function(df, a, b, c, d) {
df %>%
group_by({{a}}, {{b}}, {{c}}) %>%
filter({{d}} >=3) %>%
summarize(Mean = mean({{d}}),
SD = sd({{d}}),
Min = min({{d}}),
Max = max({{d}}),
Total = length({{d}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
```
```{r, include=TRUE}
symptom_tbl(one_impact_test_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Two Tests - Test 1
```{r, include=TRUE}
symptom_tbl(two_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Two Tests - Test 2
```{r, include=TRUE}
symptom_tbl(two_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Three Tests - Test 1
```{r, include=TRUE}
symptom_tbl(three_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Three Tests - Test 2
```{r, include=TRUE}
symptom_tbl(three_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
### Three Tests - Test 3
```{r, include=TRUE}
symptom_tbl(three_impact_test_3_symptoms, gender, cluster, symptom, post_injury_3_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Four Tests - Test 1
```{r, include=TRUE}
symptom_tbl(four_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Four Tests - Test 2
```{r, include=TRUE}
symptom_tbl(four_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
### Four Tests - Test 3
```{r, include=TRUE}
symptom_tbl(four_impact_test_3_symptoms, gender, cluster, symptom, post_injury_3_rating)
```
### Four Tests - Test 4
```{r, include=TRUE}
symptom_tbl(four_impact_test_4_symptoms, gender, cluster, symptom, post_injury_4_rating)
```
# Cluster Comparisons
Sidebar {.sidebar}
------------
Symptom Cluster Potential Symptom Severity Range:
1. Headache-Migraine Cluster: 0 - 18 (3 symptoms)
2. Cognitive Cluster: 0 - 24 (4 symptoms)
3. Anxiety-Mood Cluster: 0 - 30 (5 symptoms)
4. Ocular-Motor Cluster: 0 - 6 (1 symptom)
5. Vestibular Cluster: 0 - 24 (4 symptoms)
6. Sleep Cluster: 0 - 30 (5 symptoms)
Row {.tabset}
-----------------------------------------------------------------------
### 1 Test Cluster Summary
```{r, include=FALSE}
clusters_one_impact_test <- one_impact_test %>%
select(c(1:8, 32:37)) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age = as.character(age))
str(clusters_one_impact_test)
# normalize <- function(x) {
# return ((x - min(x)) / (max(x) - min(x)))
# }
rescale01b <- function(x) {
if(is.numeric(x)) {
z <- na.omit(x)
min_z <- min(z)
max_z <- max(z)
(z - min_z) / (max_z - min_z)
}
else {
return()
}
}
rescale01b(c(0, 5, 10))
clusters_one_impact_test_scaled <- map_df(clusters_one_impact_test, rescale01b)
clusters_one_impact_test_scaled <- bind_cols(clusters_one_impact_test,
clusters_one_impact_test_scaled)
clusters_one_impact_test_scaled <- clusters_one_impact_test_scaled %>%
select(-c(9:14))
clusters_one_impact_test_scaled_long <- clusters_one_impact_test_scaled %>%
pivot_longer(
cols = c(9:14),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
```
```{r, include=FALSE}
#summary
cluster_smry_tbl <- function(df, x) {
df %>%
group_by(symptom_cluster) %>%
summarize(mean = mean({{x}}),
sd = sd({{x}}),
min = min({{x}}),
max = max({{x}}),
total = length({{x}})) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd),
min = as.numeric(min),
max = as.numeric(max),
total = as.numeric(total)) %>%
reactable(
columns = list(
symptom_cluster = colDef(name = "Symptom Cluster",
align = "center"),
mean = colDef(name = "Mean",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
sd = colDef(name = "SD",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
min = colDef(name = "Min",
align = "center",
format = colFormat(separators = TRUE)),
max = colDef(name = "Max",
align = "center",
format = colFormat(separators = TRUE)),
total = colDef(name = "Total",
align = "center",
format = colFormat(separators = TRUE, suffix = " concussions"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
cluster_smry_tbl(clusters_one_impact_test_scaled_long, post_injury_1_score)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_one_impact_test_scaled_long, post_injury_1_score)
```
### 1 Test Cluster Boxplot
```{r, include=FALSE}
cluster_bxp_1 <- ggplot(clusters_one_impact_test_scaled_long,
aes(symptom_cluster, post_injury_1_score)) +
geom_boxplot() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25))
cluster_bxp <- function(df, x, y) {
ggplot(df, aes({{x}}, {{y}})) +
geom_boxplot() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25))
}
cluster_bxp(clusters_one_impact_test_scaled_long, symptom_cluster, post_injury_1_score)
```
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_one_impact_test_scaled_long, symptom_cluster, post_injury_1_score))
```
### 1 Test Cluster ANOVA
```{r, include=FALSE}
cluster_one_test_aov <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_one_impact_test_scaled_long)
summary(cluster_one_test_aov)
cluster_one_test_aov
```
```{r, include=TRUE}
summary(cluster_one_test_aov)
```
### 1 Test Cluster PWC
```{r, include=FALSE}
cluster_one_test_aov_pwc <- clusters_one_impact_test_scaled_long %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_one_test_aov_pwc <- cluster_one_test_aov_pwc %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_one_test_aov_pwc %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 2 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_two_impact_test_1 <- two_impact_test %>%
select(c(1:12, contains("_post_injury_1"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2))
clusters_two_impact_test_1 <- clusters_two_impact_test_1 %>%
select(c(1:12, 36:41))
clusters_two_impact_test_1_scaled <- map_df(clusters_two_impact_test_1, rescale01b)
clusters_two_impact_test_1_scaled <- bind_cols(clusters_two_impact_test_1,
clusters_two_impact_test_1_scaled)
clusters_two_impact_test_1_scaled <- clusters_two_impact_test_1_scaled %>%
select(-c(13:18))
clusters_two_impact_test_1_long <- clusters_two_impact_test_1_scaled %>%
pivot_longer(
cols = c(13:18),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_two_impact_test_2 <- two_impact_test %>%
select(c(1:12, contains("_post_injury_2"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2))
clusters_two_impact_test_2 <- clusters_two_impact_test_2 %>%
select(c(1:12, 36:41))
clusters_two_impact_test_2_scaled <- map_df(clusters_two_impact_test_2, rescale01b)
clusters_two_impact_test_2_scaled <- bind_cols(clusters_two_impact_test_2,
clusters_two_impact_test_2_scaled)
clusters_two_impact_test_2_scaled <- clusters_two_impact_test_2_scaled %>%
select(-c(13:18))
clusters_two_impact_test_2_long <- clusters_two_impact_test_2_scaled %>%
pivot_longer(
cols = c(13:18),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_two_impact_test_complete <- left_join(clusters_two_impact_test_1_long,
clusters_two_impact_test_2_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_two_impact_test_complete, post_injury_1_score)
```
### 2 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 2 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_two_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_two_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_two_tests_aov_1)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_two_tests_aov_pwc_1 <- clusters_two_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_two_tests_aov_pwc_1 <- cluster_two_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_two_tests_aov_pwc_1 %>%
reactable()
```
### 2 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_two_impact_test_complete, post_injury_2_score)
```
### 2 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 2 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_two_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_two_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_two_tests_aov_2)
```
### 2 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_two_tests_aov_pwc_2 <- clusters_two_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_two_tests_aov_pwc_2 <- cluster_two_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_two_tests_aov_pwc_2 %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 3 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_three_impact_test_1 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_1"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_1 <- clusters_three_impact_test_1 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_1_scaled <- map_df(clusters_three_impact_test_1, rescale01b)
clusters_three_impact_test_1_scaled <- bind_cols(clusters_three_impact_test_1,
clusters_three_impact_test_1_scaled)
clusters_three_impact_test_1_scaled <- clusters_three_impact_test_1_scaled %>%
select(-c(17:22))
clusters_three_impact_test_1_long <- clusters_three_impact_test_1_scaled %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_three_impact_test_2 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_2"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_2 <- clusters_three_impact_test_2 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_2_scaled <- map_df(clusters_three_impact_test_2, rescale01b)
clusters_three_impact_test_2_scaled <- bind_cols(clusters_three_impact_test_2,
clusters_three_impact_test_2_scaled)
clusters_three_impact_test_2_scaled <- clusters_three_impact_test_2_scaled %>%
select(-c(17:22))
clusters_three_impact_test_2_long <- clusters_three_impact_test_2_scaled %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_three_impact_test_3 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_3"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_3 <- clusters_three_impact_test_3 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_3_scaled <- map_df(clusters_three_impact_test_3, rescale01b)
clusters_three_impact_test_3_scaled <- bind_cols(clusters_three_impact_test_3,
clusters_three_impact_test_3_scaled)
clusters_three_impact_test_3_scaled <- clusters_three_impact_test_3_scaled %>%
select(-c(17:22))
clusters_three_impact_test_3_long <- clusters_three_impact_test_3_scaled %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
clusters_three_impact_test_join_1 <- left_join(clusters_three_impact_test_1_long,
clusters_three_impact_test_2_long)
clusters_three_impact_test_complete <- left_join(clusters_three_impact_test_join_1,
clusters_three_impact_test_3_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_1_score)
```
### 3 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 3 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_three_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_1)
```
### 3 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_three_tests_aov_pwc_1 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_1 <- cluster_three_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_1 %>%
reactable()
```
### 3 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_2_score)
```
### 3 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 3 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_three_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_2)
```
### 3 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_three_tests_aov_pwc_2 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_2 <- cluster_three_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_2 %>%
reactable()
```
### 3 Tests Cluster Summary - Test 3
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_3_score)
```
### 3 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_3_score))
```
### 3 Tests Cluster ANOVA - Test 3
```{r, include=FALSE}
cluster_three_tests_aov_3 <- aov(post_injury_3_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_3)
```
### 3 Tests Cluster PWC - Test 3
```{r, include=FALSE}
cluster_three_tests_aov_pwc_3 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_3_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_3 <- cluster_three_tests_aov_pwc_3 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_3 %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 4 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_four_impact_test_1 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_1"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_1 <- clusters_four_impact_test_1 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_1_scaled <- map_df(clusters_four_impact_test_1, rescale01b)
clusters_four_impact_test_1_scaled <- bind_cols(clusters_four_impact_test_1,
clusters_four_impact_test_1_scaled)
clusters_four_impact_test_1_scaled <- clusters_four_impact_test_1_scaled %>%
select(-c(21:26))
clusters_four_impact_test_1_long <- clusters_four_impact_test_1_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_four_impact_test_2 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_2"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_2 <- clusters_four_impact_test_2 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_2_scaled <- map_df(clusters_four_impact_test_2, rescale01b)
clusters_four_impact_test_2_scaled <- bind_cols(clusters_four_impact_test_2,
clusters_four_impact_test_2_scaled)
clusters_four_impact_test_2_scaled <- clusters_four_impact_test_2_scaled %>%
select(-c(21:26))
clusters_four_impact_test_2_long <- clusters_four_impact_test_2_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_four_impact_test_3 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_3"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_3 <- clusters_four_impact_test_3 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_3_scaled <- map_df(clusters_four_impact_test_3, rescale01b)
clusters_four_impact_test_3_scaled <- bind_cols(clusters_four_impact_test_3,
clusters_four_impact_test_3_scaled)
clusters_four_impact_test_3_scaled <- clusters_four_impact_test_3_scaled %>%
select(-c(21:26))
clusters_four_impact_test_3_long <- clusters_four_impact_test_3_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
clusters_four_impact_test_4 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_4"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_4 <- clusters_four_impact_test_4 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_4_scaled <- map_df(clusters_four_impact_test_4, rescale01b)
clusters_four_impact_test_4_scaled <- bind_cols(clusters_four_impact_test_4,
clusters_four_impact_test_4_scaled)
clusters_four_impact_test_4_scaled <- clusters_four_impact_test_4_scaled %>%
select(-c(21:26))
clusters_four_impact_test_4_long <- clusters_four_impact_test_4_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_4_score",
names_pattern = "(.*)_score_post_injury_4"
)
clusters_four_impact_test_join_1 <- left_join(clusters_four_impact_test_1_long,
clusters_four_impact_test_2_long)
clusters_four_impact_test_join_2 <- left_join(clusters_four_impact_test_join_1,
clusters_four_impact_test_3_long)
clusters_four_impact_test_complete <- left_join(clusters_four_impact_test_join_2,
clusters_four_impact_test_4_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_1_score)
```
### 4 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 4 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_four_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_1)
```
### 4 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_four_tests_aov_pwc_1 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_1 <- cluster_four_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_1 %>%
reactable()
```
### 4 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_2_score)
```
### 4 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 4 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_four_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_2)
```
### 4 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_four_tests_aov_pwc_2 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_2 <- cluster_four_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_2 %>%
reactable()
```
### 4 Tests Cluster Summary - Test 3
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_3_score)
```
### 4 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_3_score))
```
### 4 Tests Cluster ANOVA - Test 3
```{r, include=FALSE}
cluster_four_tests_aov_3 <- aov(post_injury_3_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_3)
```
### 4 Tests Cluster PWC - Test 3
```{r, include=FALSE}
cluster_four_tests_aov_pwc_3 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_3_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_3 <- cluster_four_tests_aov_pwc_3 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_3 %>%
reactable()
```
### 4 Tests Cluster Summary - Test 4
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_4_score)
```
### 4 Tests Cluster Boxplot - Test 4
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_4_score))
```
### 4 Tests Cluster ANOVA - Test 4
```{r, include=FALSE}
cluster_four_tests_aov_4 <- aov(post_injury_4_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_4)
```
### 4 Tests Cluster PWC - Test 4
```{r, include=FALSE}
cluster_four_tests_aov_pwc_4 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_4_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_4 <- cluster_four_tests_aov_pwc_4 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_4 %>%
reactable()
```
